Best AI tools for< Search Mathlib4 >

Moogle is a semantic search tool designed to help users find theorems related to mathlib4 quickly and efficiently. It utilizes advanced algorithms to understand the context of mathematical queries and provide relevant results. Moogle is a powerful tool for researchers, students, and anyone working with mathematical theorems.

Talpa Search is a search engine developed by LibraryThing that allows users to search for books, movies, music, and various other topics. Users can search for a wide range of queries, from specific book quotes to movie genres. The platform aims to provide a user-friendly search experience for individuals looking to explore different media and information.

The website is a platform that offers an AI Video Search Engine. Users can index videos, sign in, and explore topics related to the human brain, Supabase, startups, AI image generation, and the future of startups. The platform has indexed 17274 videos totaling 277753 minutes. Users can view the code on Github or follow the creator on social media.

The website is an AI tool that allows users to search for answers directly inside thousands of YouTube videos. It is a free-of-cost, easy-to-navigate, and fast tool that leverages AI technology to provide efficient search results. Users can quickly find information from videos without the need to watch the entire content.

Search Alkemy is a free AI-powered SEO keyword research and topic clustering tool that helps content marketers and SEOs discover high-performing keywords, analyze search intent, and create content that ranks. With Search Alkemy, you can:

Search&AI is a comprehensive platform designed for patent due diligence, offering efficient and accurate results in minutes. It provides services such as prior art search, claim chart generation, novelty diligence analysis, portfolio analysis, document search, and AI-powered chatbot assistance. The platform is built by a team of experienced engineers and is tailored to streamline the patent discovery and analysis process, saving time and money compared to traditional outsourced search firms.

LimeWire Search is an AI-powered platform that offers a range of creative tools to assist users in generating visual and audio content. Users can create abstract images, convert text to images, edit images, remove backgrounds, outpaint and inpaint images, upscale image quality, and create music from text or images. LimeWire Search aims to empower users with AI technology to enhance their creativity and productivity in visual and audio content creation.

Felo Search is a free AI search engine developed by Sparticle Inc. It utilizes artificial intelligence technology to provide users with accurate and relevant search results. The platform is designed to enhance the search experience by understanding user queries and delivering personalized results. Felo Search aims to revolutionize the way people search for information online by leveraging AI algorithms to improve search efficiency and effectiveness.

AI Search is a comprehensive AI tools database that helps users discover and explore a wide range of AI tools and applications. With over 13000 AI tools listed and updated daily, AI Search provides a valuable resource for individuals and businesses seeking to leverage AI technologies. The platform allows users to search for AI tools based on specific functions or keywords, making it easy to find the right tool for their needs. AI Search also offers a newsletter service that delivers top updates in AI directly to users' inboxes every weekend.

SAO (Search Ads Optimization) is an AI optimization platform designed for Apple Search Ads campaigns. It leverages AI-based automation to optimize and automate keyword bid management, helping users scale revenue, improve ROAS, and reduce cost per acquisition. The platform offers in-depth analytics, data visualization, and advanced ASO tools to enhance campaign performance and efficiency. Trusted by app developers, agencies, and consultants worldwide, SAO is a game-changer in maximizing ad spend and driving better results with minimal effort.

Biblos Semantic Bible Search & Summary is an AI-powered tool that offers a powerful Bible search experience. It provides semantic search capabilities and a powerful understanding model to enhance the user's exploration of the Bible. The tool aims to deliver lightning-fast searches and insightful summaries of both the Old Testament and New Testament.

BoCha AI Search is a multi-modal AI search engine that provides instant answers to your queries. It leverages advanced AI technology to deliver accurate and comprehensive results, making it an indispensable tool for researchers, students, and professionals alike.

ChatGPT for Search Engines is an extension that allows users to display ChatGPT responses alongside search engine results. It also provides a range of ChatGPT prompt templates to help users optimize their experiences with ChatGPT. The extension is available on popular browsers such as Google Chrome, Mozilla Firefox, and Microsoft Edge.

Ai Prompt Search is a website that provides users with a search engine for AI art prompts. The website includes prompts for various AI art models, including Stable Diffusion, Midjourney, and Openjourney. Users can search for prompts by topic, such as anime, fashion, architecture, landscapes, interior design, 3D renders, and character design. The website also includes a section with frequently asked questions about AI art prompts.

This website provides an AI image generator and search engine that allows users to generate and find AI-generated images for free. The AI image generator can create images from text prompts, and the search engine can be used to find existing AI-generated images. The website is easy to use and does not require any registration.

Quick GPT Search is an AI-powered language model application that integrates with search engines to provide natural language responses to user queries. It utilizes OpenAI's ChatGPT to generate human-like writing and offer contextually appropriate answers. The extension allows users to access ChatGPT directly from search engine results, enabling seamless interaction with the language model. With features like Popup Mode, Trigger Settings, Dark Theme, and Code Syntax highlighting, Quick GPT Search enhances the search experience by simplifying information retrieval and promoting user engagement.

The website is an AI tool called Google Lens that allows users to search, discover, and explore the world around them using AI-powered technology. Users can identify plants, search for information, shop, translate text, find songs, and more by simply using their camera or voice. Google Lens provides detailed overviews, helps with homework, and offers a unique way to interact with the environment through augmented reality. With 25 years of search history, Google Lens continues to innovate and inspire users worldwide.

Google is a search engine that helps users find information on the World Wide Web. It is the most popular search engine in the world, with over 90% of the global search market share. Google also offers a variety of other products and services, including Gmail, Google Drive, and Google Maps.

Google Patents is a search engine that allows users to search through the full text of patents that have been granted by the United States Patent and Trademark Office (USPTO). The database includes patents from 1790 to the present day, and users can search by keyword, inventor, assignee, or patent number. Google Patents also provides access to images of the original patent documents, as well as links to related patents and articles.

Google Lens is an AI-powered visual search tool developed by Google that allows users to search, shop, translate, and identify objects using their camera or images. With Google Lens, users can find similar clothes, furniture, and home decor, translate text in real-time from over 100 languages, get step-by-step homework help for various subjects, and identify plants and animals. The application is available on all devices and in various Google apps, making it convenient for users to access its features anytime, anywhere.

This prototype synthesizes verified code with an LLM using Monte Carlo Tree Search (MCTS). It explores the space of possible generation of a verified program and checks at every step that it's on the right track by calling the verifier. This prototype uses Dafny, Coq, Lean, Scala, or Rust. By using this technique, weaker models that might not even know the generated language all that well can compete with stronger models.

Lean Copilot is a tool that enables the use of large language models (LLMs) in Lean for proof automation. It provides features such as suggesting tactics/premises, searching for proofs, and running inference of LLMs. Users can utilize built-in models from LeanDojo or bring their own models to run locally or on the cloud. The tool supports platforms like Linux, macOS, and Windows WSL, with optional CUDA and cuDNN for GPU acceleration. Advanced users can customize behavior using Tactic APIs and Model APIs. Lean Copilot also allows users to bring their own models through ExternalGenerator or ExternalEncoder. The tool comes with caveats such as occasional crashes and issues with premise selection and proof search. Users can get in touch through GitHub Discussions for questions, bug reports, feature requests, and suggestions. The tool is designed to enhance theorem proving in Lean using LLMs.

Build your own conversation-based search with AI, a simple implementation with Node.js & Vue3. Live Demo Features: * Built-in support for LLM: OpenAI, Google, Lepton, Ollama(Free) * Built-in support for search engine: Bing, Sogou, Google, SearXNG(Free) * Customizable pretty UI interface * Support dark mode * Support mobile display * Support local LLM with Ollama * Support i18n * Support Continue Q&A with contexts.

LLM_Web_search project gives local LLMs the ability to search the web by outputting a specific command. It uses regular expressions to extract search queries from model output and then utilizes duckduckgo-search to search the web. LangChain's Contextual compression and Okapi BM25 or SPLADE are used to extract relevant parts of web pages in search results. The extracted results are appended to the model's output.

Aipan-Netdisk-Search is a free and open-source web project for searching netdisk resources. It utilizes third-party APIs with IP access restrictions, suggesting self-deployment. The project can be easily deployed on Vercel and provides instructions for manual deployment. Users can clone the project, install dependencies, run it in the browser, and access it at localhost:3001. The project also includes documentation for deploying on personal servers using NUXT.JS. Additionally, there are options for donations and communication via WeChat.

Kumo search is an end-to-end search engine framework that supports full-text search, inverted index, forward index, sorting, caching, hierarchical indexing, intervention system, feature collection, offline computation, storage system, and more. It runs on the EA (Elastic automic infrastructure architecture) platform, enabling engineering automation, service governance, real-time data, service degradation, and disaster recovery across multiple data centers and clusters. The framework aims to provide a ready-to-use search engine framework to help users quickly build their own search engines. Users can write business logic in Python using the AOT compiler in the project, which generates C++ code and binary dynamic libraries for rapid iteration of the search engine.

Build your own conversational search engine using less than 500 lines of code. Features built-in support for LLM, search engine, customizable UI interface, and shareable cached search results. Setup includes Bing and Google search engines. Utilize LLM and KV functions with Lepton for seamless integration. Easily deploy to Lepton AI or your own environment with one-click deployment options.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

pyLLMSearch is an advanced RAG system that offers a convenient question-answering system with a simple YAML-based configuration. It enables interaction with multiple collections of local documents, with improvements in document parsing, hybrid search, chat history, deep linking, re-ranking, customizable embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) from OpenAI or installed locally. It supports various document formats, incremental embedding updates, dense and sparse embeddings, multiple embedding models, 'Retrieve and Re-rank' strategy, HyDE (Hypothetical Document Embeddings), multi-querying, chat history, and interaction with embedded documents using different models. It also offers simple CLI and web interfaces, deep linking, offline response saving, and an experimental API.

This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

A Rust based tool to evaluate LLM models, prompts and model params. It automates the process of selecting the best model parameters, given an LLM model and a prompt, iterating over the possible combinations and letting the user visually inspect the results. The tool assumes the user has Ollama installed and serving endpoints, either in `localhost` or in a remote server. Key features include: * Automatically fetches models from local or remote Ollama servers * Iterates over different models and params to generate inferences * A/B test prompts on different models simultaneously * Allows multiple iterations for each combination of parameters * Makes synchronous inference calls to avoid spamming servers * Optionally outputs inference parameters and response metadata (inference time, tokens and tokens/s) * Refetching of individual inference calls * Model selection can be filtered by name * List experiments which can be downloaded in JSON format * Configurable inference timeout * Custom default parameters and system prompts can be defined in settings

Local-GenAI Search is a local generative search engine powered by the Llama3 model, allowing users to ask questions about their local files and receive concise answers with relevant document references. It utilizes MS MARCO embeddings for semantic search and can run locally on a 32GB laptop or computer. The tool can be used to index local documents, search for information, and provide generative search services through a user interface.

This starter project is designed to process `.mdx` files in the `pages` directory to use as custom context within OpenAI Text Completion prompts. It involves building a custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase. The project includes steps for pre-processing knowledge base, storing embeddings in Postgres, performing vector similarity search, and injecting content into OpenAI GPT-3 text completion prompt.

Sourcegraph is a code search and navigation tool that helps developers read, write, and fix code in large, complex codebases. It provides features such as code search across all repositories and branches, code intelligence for navigation and refactoring, and the ability to fix and refactor code across multiple repositories at once.

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

AI Search Engine - About us & for Advertising at DigitalMediaMarketing.Agency

I conjure advanced Google search queries.

Analyzes GSC data and highlights the impact of search updates.

Creates Google Ads headlines in bulk based on direct response copy principles.

Search data on the impact of AI on jobs, productivity and operations published by Synthetic Work (https://synthetic.work)

Analyse content through the official Google Search Quality Rater Guidelines.

Refines search queries with specific terms and includes Google links

Your go-to expert for web searches.

Proustian Chatbot with Full French & English Volumes

GPT search. Click ["] to start working.

Browse the web for the search term and summarize the results from sources

Optimized with advanced search operators for refined results. Specializing in finding and linking top custom GPTs from builders around the world. Version 0.3.0

Create the most effective database or search engine queries using keywords, truncation, and Boolean operators!

Expert en Islam offrant des conseils détaillés sur la base du Saint Coran et des Hadiths

This GPT answers all the questions related to Google Search Console

OnlyFans Search Engine, provided by FansIQ.com

Expert in tailored accelerator search for startups.

I'm Quora Search, specialized in finding Quora answers and insights.

Summarize about the topic based on scientifically sound information

Make an informed choice about brand cruelty-free status